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February 28, 2026IEEE Transactions on Pattern Analysis and Machine Intelligence

Deployment Prior Injection for Run-time Re-biasable Object Detection

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Authors

MZMo ZhouYYYiding YangHLHaoxiang Li

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Overview

This approach demonstrates improved object detection by adapting to shifting environments using graph-based context prior.

Key Points

  • The research aims to enhance object detection by addressing training set bias using deployment context priors.
  • Introduced a graph input to represent deployment context prior and object relations.
  • Modified the training objective to bind the detector behavior to the graph.
  • Enabled run-time re-biasing of the detector without parameter updates.
  • Achieved effective detection using deployment context prior on the COCO dataset.
  • Demonstrated success in cross-dataset testing on the Objects365 dataset.
  • Showed that the detector can self-re-bias with approximated deployment priors.

Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/69a286240a974eb0d3c00e62https://doi.org/10.1109/tpami.2026.3667914
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